Award Data

krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

Multi-Dimensional Event Sourcing & Correlation - Publicly Available Information (PAI) (MDESC-P) will support collection jointly across disparate PAI sources with coordinated cueing of more constrained intelligence, surveillance, target acquisition, and reconnaissance (ISTAR) sources. The primary objective for MDESC-P is to deliver a scalable and automated PAI collection management solution using a ...

Deep Labs recognizes USSOCOM’s challenge to process multiple data and communications inputs for optimized decision making, and to support rapid on-the-move abilities to learn and communicate knowledge to enhance tactically relevant situational awareness in peer/near peer environments. Deep Labs has proven this capability across complex challenges in the world’s largest commercial enterprises a ...

Quantum technology will become a key enabler of future Air Force superiority. Topological insulator (TI) qubits are inherently stable and fault-tolerant because they exploit local topological symmetries and global boundary conditions of chalcogenide materials to yield unique, emergent quantum states. Wake Forest University and Streamline Automation have been working collaboratively for the last se ...

Improving the effectiveness of counter-WMD operations requires improved understanding of weapon-target interaction. Specifically, time-resolved measurements of temperature and composition are required to allow temporal evolution of a detonation fireball. To address this need, SA Photonics will develop MONITOR, a laser-based temperature diagnostic which will enable wide dynamic range temperature me ...

The Synthetic Aperture Radar (SAR) Image Generation Data Augmentation (SIGDA) system is achieved using SAR simulators and the Arete’s Reification approach. Large, realistic datasets will be generated using the Arete Reification capability. These large Reified datasets are then used to train machine learning or Artificial Intelligence (AI), Automatic Target Recognition (ATR) classification algori ...

The overall goal is to develop numerics-informed neural networks (NINNs) and DeepOnets for chemical reactions and for PDEs with spatial derivatives improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants. Based on the first NINN developed by the Karniadakis’s group in 2018, which blends the multi-step time-stepping with deep neural networks, ...

For effective protection against radiated threats, produced by high altitude electromagnetic pulse (HEMP) caused by nuclear detonations and high-power microwave (HPM) Directed Energy (DE) weapons it is important to understand not only the physics of the threats, but also to quantify the effects on mission-critical electrical systems. EMP/HMP simulators enable threat level testing of MCS and provid ...

The US requires standoff precision strike capabilities in GPS-denied and high threat environments. This includes fire-and-forget lock-after-launch vision-based guidance for SOPGM. Due to emerging threats, a paradigm shift is occurring in the way we gather intelligence, maintain surveillance, and perform reconnaissance. ISR platforms are evolving, and artificial intelligence is at the forefront of ...